The exemplary embodiment relates to call centers and finds particular application in connection with a system and method for steering incoming calls to a real or virtual agent based on an utterance of a customer.
Call centers are used by a variety of businesses and other organizations to assist a customer, such as an existing customer or prospective customer, by answering a query, completing a transaction, or the like. In a traditional call center, a number of agents may be available to conduct dialogues with customers. In an inbound call center, the dialogue is generally initiated by the customer by telephone. Such call centers tend to be labor-intensive and thus costly to run. Additionally, because of logistic and economic constraints, inbound call centers may be physically housed across several different locations, time zones, and countries.
A call center can be defined as a set of resources (persons and infrastructure), which enable the delivery of services via the telephone (Pierre L'Ecuyer, “Modeling and optimization problems in contact centers, 3rd Int'l Conf. on Quantitative Evaluation of Systems, pp. 145-156 (2006); Zeynep Aksin, et al., “The modern call center: A multi-disciplinary perspective on operations management research,” Production and Operations Management, 16 (6):665-688 (2007); Wyean Chan, et al., “Call center routing policies using call waiting and agent idle times,” Manufacturing & Service Operations Management, 16 (4):544-560 (2014)). Most call centers also support Interactive Voice Response (IVR) units (Polyna Khudyakov, et al., “Designing a call center with an IVR (interactive voice response),” Queueing Syst., 66 (3):215-237 (2010), which can be summarized as answering machines, including predefined and limited possibilities of interactions. Most major companies have organized their communication with customers via one or more call centers, either internally-managed or outsourced. Call centers are commonly identified along several dimensions: functionalities (help desk, emergency, telemarketing, information providers, etc.), capacity, geographic distribution (single vs. multi-location) and agents characteristics (low-skilled vs. highly-trained agents and single vs. multi-skilled agents).
Recently, near-automatic solutions of assistance through autonomous dialog platforms, called virtual agents, have been developed, which are made possible through advances in automatic speech recognition (ASR) and statistical dialog systems. See, Jason D. Williams, et al., “Partially observable Markov decision processes for spoken dialog systems,” Computer Speech & Language, 21 (2):393-422 (2007); J. D. Williams, et al., “Scaling POMDPs for spoken dialog management,” IEEE Trans. on Audio, Speech & Language Processing, 15 (7):2116-2129 (2007); Steve J. Young, et al., “POMDP-based statistical spoken dialog systems: A review,” Proc. IEEE, 101 (5):1160-1179 (2013).
However, call centers often face varied demands, like question answering, transactional requests, and troubleshooting diagnostics. Developing automated systems to handle all of these demands is challenging. One way to address this could be to equip each virtual agent with a human-fallback capability, where difficult situations during the dialog would be automatically detected in order to re-route the calls to human agents. See, Edward Filisko, et al., “Error detection and recovery in spoken dialogue systems,” Proc. Workshop On Spoken Language Understanding For Conversational Systems, pp. 31-38 (2004); Rolf Carlson, et al., “Error handling in spoken dialogue systems,” Speech Communication, 45 (3):207-209 (2005); Caroline Bousquet-Vernhettes, et al., “Recognition error handling by the speech understanding system to improve spoken dialogue systems,” ISCA Tutorial and Research Workshop on Error Handling in Spoken Dialogue Systems, pp. 113-118 (2003); Dan Bohus, Ph.D. Thesis: “error awareness and recovery in task-oriented spoken dialog systems” (2014).
Such solutions have several problems. In particular, detection of a situation where the automated dialog agent faces difficulty entails complex analysis of the actual dialogue and discourse structure to accurately assess the necessity of a human re-routing. Additionally, the re-routing of a call indicates a failure to the calling customer that could affect the customer's confidence in the overall call-center problem solving capability. The customer may become frustrated by having to repeat the same information to the human agent. In turn, the human agent may find it more difficult to conduct the dialogue than if the customer had been routed directly to the agent, particularly if the human agent has to spend time in analyzing the human-machine conversation which has already taken place, in order to be able to continue it.
It would be desirable, therefore, to integrate virtual and real (human) agents into a common operation, with real agents handling the inbound calls that are less suited to being handled by virtual agents. In order to organize such heterogeneous agent environments efficiently, it would be advantageous to minimize the probability of failure of virtual agent-handled calls by accurately assessing the nature and the complexity level of a given inbound call from the beginning of the customer dialogue while considering the cost of human handled calls. The calls could then be steered to the appropriate type of agent.
Automatic analysis and classification of calls in the context of call centers has been widely studied in the domain of quality monitoring, error detection and categorization. See, e.g., Patrick Haffner, et al., “Optimizing SVMs for complex call classification,” ICASSP, pp. 632-635, IEEE (2003); Geoffrey Zweig, et al., “Automated quality monitoring in the call center with ASR and maximum entropy,” ICASSP, pp. 589-592, IEEE (2006), Fernando Uceda-Ponga, et al., “A misclassification reduction approach for automatic call routing,” MICAI 2008: Advances in Artificial Intelligence, pp. 185-192 (2008); Youngja Park, et al., “Low-cost call type classification for contact center calls using partial transcripts,” INTERSPEECH, pp. 2739-2742 (2009); Dilek Hakkani-Tür, et al., “Unsupervised and active learning in automatic speech recognition for call classification,” ICASSP, pp. 429-432, IEEE (2004). More broadly, operational research has used the context of call centers for scheduling and more generally resource allocation researches. Ger Koole, et al., “Queuing models of call centers: An introduction,” Annals of Operations Research, 113 (1), 41-59 (2002); Zeynep Aksin, et al., “The modern call center: A multi-disciplinary perspective on operations management research,” Production and Operations Management, 16 (6):665-688 (2007); Achal Bassamboo, et al., “On a data-driven method for staffing large call centers,” Operations Research, 57 (3):714-726 (2009), also called staffing. Skill-based routing has also been largely studied in the domain of heterogeneous skilled human agent centers. Rodney B. Wallace, et al., “Comparing skill-based routing call center simulations using C programming and arena models,” Proc. 37th Conf. on Winter Simulation, pp. 2636-2644 (2005); Ger Koole, et al., “Approximate dynamic programming in multi-skill call centers,” Proc. 2005 Winter Simulation Conference, pp. 576-583 (2005); Jaroslaw Bylina, et al., “A Markovian model of a call center with time varying arrival rate and skill based routing,” Computer Networks, pp. 26-33 (2009). In such a context, the main purpose is to optimize the utilization of the agents by considering their skills while routing the customer incoming calls to the most appropriate one. Content based call-steering has been studied in the domain of human populated customer care in order to assess the nature of inbound calls more efficiently.
However, none of these methods addresses steering of calls to real and virtual agents at the outset of the dialogue, before a failure of a virtual agent has occurred.